The use of shape as an anatomical object property is a rapidly increasing portion of research in the field of medical image analysis. Shape representations and shape models have been used in connection with segmentation of medical images, diagnosis, and motion analysis. Among different types of shape models, Active Shape Models (ASMs) have been frequently applied and proven a powerful tool for characterizing objects and segmenting medical images. In order to construct such models, sets of labeled training images are required. The labels in the training sets consist of landmark points defining the correspondences between similar structures in each image across the set. Manual definition of landmarks on 2D shapes has proven to be both time-consuming and error prone. To reduce the burden, semi-automatic systems have been developed. In these systems, a model is built from the current set of examples, and used to search the next image. The user can edit the result where necessary, then add the example to the training set. Though this can considerably reduce the time and effort required, labeling large sets of examples is still labor intensive.
Because of the importance of landmark labeling, a few attempts have been made to automate the shape alignment/average process. For example, Lorenz and Krahnstover automatically locate candidates for landmarks via a metric for points of high curvature, Lorenz C., Krahnstove N. Generation of point-based 3D statistical shape models for anatomical objects. CVIU, vol. 77 no. 2, February 2000, pp. 175-191. Davatzikos et al. used curvature registration on contours produced by an active contour approach, (C. Davatzikos, M. Vaillant, S. M. Resnich, J. L. Prince, S. Letovsky, and R. N. Bryan, A Computerized Approach for Morphological Analysis of the Corpus Callosum, J. Computer Assisted Tomography vol. 20, 1996, pp. 88-97). Duncan et al. (J. Duncan, R. L. Owen, L. H. Staib, and F. Anandan, Measurement of non-rigid motion using contour shape descriptors, in IEEE Conference on Computer Vision and Pattern Recognition, 1991, pp. 318-324). And Kambhamettu et al, (C. Kambhamettu and D. B. Goldgof, Point correspondence recovery in non-rigid motion, IEEE Conference on Computer Vision and Pattern Recognition, 1992, pp. 545-561), propose methods of correspondence based on the minimization of a cost function that involves the difference in the curvature of two boundaries. However, as pointed out by several studies, curvature is a rigid invariant of shape and its applicability is limited in case of nonlinear shape distortions. In addition, it is hard to find sufficient high curvature points on lung contours.
Hill et al. employed a sparse polygonal approximation to one of two boundaries which is transformed onto the other boundary via an optimization scheme, (A. Hill, C. J. Taylor, and A. D. Brett, A Framework for Automatic Landmark Identification Using a New Method of Nonrigid Correspondence, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 3, 2000, pp. 241-251). The polygonal matching is based on an assumption that arc path-lengths between consecutive points are equal. This assumption may be violated in case of severe shape difference and is especially difficult to satisfy in polygonal approximation of lung shape contours.
As a result, the prior art does not fit the lung shape modeling very well, therefore there exists a need for a method for automatically constructing 2D statistical shape model of lung regions in chest radio graphs.